[Sparse Coding] Sparse Coding sparse expression paper list

Http://www.sigvc.org/bbs/thread-57-1-1.html

1 What is the goal of sensory coding:
This paper introduces two sensory coding methods, PCA and Sparse Coding, compares their advantages and disadvantages, and points out that for most biological information processing, Sparse Coding is usually used.
2 Sparse Coding with an overcomplete basis set a strategy employed by V1: (Dai Wei)
Author: Examine the neruobiological implications of Sparse Coding
3 emergence of simple-cell semantic tive field properties by learning a sparse code for Natural Images: (Li haichang)
It is pointed out that for natural images, the Set (that is, the dictionary) corresponding to the sparse representation is the basis of the Gabor filter class. Code is available.
4 regression shrinkage and selection via the lasso (Xiao Hongfei, Kang Cuicui)
Lasso article
5 atomic decomposition by basis pursuit (Dai Wei)
A Method for Solving sparse: linear programming.
6 non-negative matrix factorization with sparseness constraints (Zhu Feiyun)
Add the sparse constraint of the coefficient to the non-negative Matrix Factorization to make the basis of the decomposition more like a part.
7 least angle Regression
8 sparse_signal_restoration (GU Xiangxiang)
Use iterated soft-thresholding algorithm to solve L1
9 A fast iterative Shrinkage-thresholding algorithm (GU Xiangxiang)
Solve L1, similar to iterated soft-thresholding algorithm, but fast
10 iteratively re-Weighted Least Squares Minimization for sparse recovery
Use iteratively re-weighted least squares to solve L1 constraints or L _ {p} Constraints
11 K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation (Xu yuan)
K-SVD dictionary
12 Image Denoising Via sparse and redundant representations over learned dictionaries (Xu yuan)
Sparse for Denoising
13 image super-resolution as sparse representation of raw image patches (Liu shaoguo)
Sparse for Super-resolution
14 robust face recognition via sparse (Xu yuan)
Sparse for face recognition
15 Compressed Sensing
Proof of equivalence between l0 and L1
16 robust uncertainty principles exact Signal Reconstruction from highly incomplete frequency information
Proof of equivalence between l0 and L1
17 enhancing sparsity by reweighted L1 Minimization
The weighted L1 method is better than L1 in many cases.
18 supervised dictionary Learning (Wang lingfeng)
Close the loop joint Blind Image Restoration and recognition with sparse representation prior:
The image in the face database is used to represent the de-Blurred face image in sparse to improve the recognition of the fuzzy face image.
Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary
Online Detection of unusual events in videos via dynamic Sparse Coding:
Similar to sparse denosing and Superresolution
Some articles at recent meetings:
Online dictionary learning for Sparse Coding: it can solve the problem of learning dictionaries from a large number of samples.
Optical Flow Estimation Using learned sparse Model
Robust tracking using local sparse appearance model and K-selection
Sparse approximated nearest points for image set classifcation
Sparse representation for Color Image Restoration:
All have: denoising, demosaicing, inpainting
Supervised dictionary learning:
In addition to the reconstructed target function, the dictionary learning also includes some discriminatory information.
Learning with structured sparsity:
A Note on the group lasso and a sparse group:
Structure sparse, which may be similar to the 2, 1 norm of the matrix
Other references: http://dsp.rice.edu/cs